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Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks.
Bartosz Miller1, Leonard Ziemiański1
1Faculty of Civil and Environmental Engineering and Architecture, Rzeszów University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland.
Materials (Basel, Switzerland)
|June 2, 2021
Summary
This study introduces a new method using convolutional neural networks (CNNs) to identify vibration mode shapes in composite structures. The approach accurately detects structural damage and material degradation, even with noisy data.
Area of Science:
- Structural Health Monitoring
- Composite Materials
- Vibration Analysis
- Machine Learning
Background:
- Traditional image analysis methods for identifying vibration mode shapes in multilayer composite structures have limitations, particularly with two-dimensional descriptions and high spatial resolution requirements.
- Existing techniques struggle to effectively analyze complex, three-dimensional structures and are often sensitive to noise and material variations.
Purpose of the Study:
- To develop and present a novel, robust approach for identifying vibration mode shapes in multilayer composite structures.
- To overcome the limitations of conventional image analysis techniques by leveraging advanced computational methods.
- To create an algorithm capable of accurately detecting material degradation and local damage within composite structures.
Main Methods:
- Application of convolutional neural networks (CNNs) to create a three-dimensional mode shapes identification algorithm.
- Development of a procedure that significantly reduces the number of required mode shape vector coordinates.
- Testing the algorithm's robustness against noisy input data and variations in material properties.
Main Results:
- The proposed CNN-based procedure demonstrates high accuracy and effectiveness in identifying vibration mode shapes.
- The method is robust to noisy input data, showing no degradation in performance.
- The algorithm successfully identifies the occurrence of local damage and material degradation without compromising accuracy.
Conclusions:
- The developed CNN-based approach offers a significant advancement in the identification of vibration mode shapes for multilayer composite structures.
- This method provides a reliable tool for structural health monitoring, capable of detecting damage and material degradation.
- The algorithm's resilience to noise and material changes makes it a practical solution for real-world applications.
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